Exploring High-Dimensional Data Space: Identifying Optimal Process Conditions in Photovoltaics: Preprint

Conference · Fri Jul 01 00:00:00 EDT 2011

DOI:https://doi.org/10.1109/PVSC.2011.6186065· OSTI ID:1018866

Suh, Changwon; Glynn, Stephen; Scharf, John; Contreras, Miguel A.; Noufi, Rommel; Jones, Wesley B.; Biagioni, David

We demonstrate how advanced exploratory data analysis coupled to data-mining techniques can be used to scrutinize the high-dimensional data space of photovoltaics in the context of thin films of Al-doped ZnO (AZO), which are essential materials as a transparent conducting oxide (TCO) layer in CuInxGa1-xSe2 (CIGS) solar cells. AZO data space, wherein each sample is synthesized from a differentprocess history and assessed with various characterizations, is transformed, reorganized, and visualized in order to extract optimal process conditions. The data-analysis methods used include parallel coordinates, diffusion maps, and hierarchical agglomerative clustering algorithms combined with diffusion map embedding.

• Research Organization: National Renewable Energy Lab. (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1018866
• Report Number(s): NREL/CP-2C00-50693; TRN: US201114%%377
• Resource Relation: Conference: Presented at the 37th IEEE Photovoltaic Specialists Conference (PVSC 37), 19-24 June 2011, Seattle, Washington
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
36 MATERIALS SCIENCE
ALGORITHMS
DATA ANALYSIS
DIFFUSION
OXIDES
SOLAR CELLS
THIN FILMS
data analysis
photovoltaics
PV